Staff Software Engineer, Core AI Infrastructure

You’ll join a high-performing team of engineers driving AI transformation at Coinbase as a Senior Software Engineer on the IT Operations team within ESTO. This team builds custom products through full-stack engineering and scales the infrastructure powering Coinbase’s AI products, with direct exposure to senior leadership in a fast-paced, incubator-style environment. You’ll own the reliability and automation of critical AI infrastructure, ensuring our systems are resilient, observable, and secure at scale.

What you’ll do:

• Own end-to-end delivery of AI products by building production-grade distributed systems, including serving infrastructure, data pipelines, and deployment orchestration across the full stack throughout the SDLC.

• Drive platform adoption by designing clean APIs, abstractions, and developer-facing tooling that enable product teams to integrate AI capabilities without bespoke infrastructure requests.

• Partner with engineering and product leadership acrossPlatformand other product groups to align infrastructure requirements, resolve cross-team technical dependencies, and define shared platform contracts.

• Shape engineering standards and technical culture by establishing architectural patterns, mentoring engineers, and raising the bar on code quality, observability, and operational excellence.

• Build full-stack applications that power internal AI products and infrastructure using Go or Python.

• 8+ years of experience automating and supporting cloud infrastructure (AWS) and network environments, with hands-on use of infrastructure-as-code tools such as Terraform, Ansible, Chef, Puppet, or Salt.

• Proven experience deploying, managing, and troubleshooting containerized workloads using Docker and Kubernetes in production environments.

• Proficiency in at least one programming language (Python, Bash, Ruby, or Go) and version control workflows using Git-based CI/CD pipelines.

• Platform-builder mindset with proven history of designing reusable infrastructure, APIs, and abstractions adopted by multiple teams across an engineering organization.

• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.